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Volumn 2, Issue January, 2014, Pages 1260-1268

Spectral methods meet EM: A provably optimal algorithm for crowdsourcing

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE; SPECTROSCOPY;

EID: 84937883035     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (245)

References (27)
  • 6
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    • Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing
    • X. Chen, Q. Lin, and D. Zhou. Optimistic knowledge gradient policy for optimal budget allocation in crowdsourcing. In Proceedings of ICML, 2013.
    • (2013) Proceedings of ICML
    • Chen, X.1    Lin, Q.2    Zhou, D.3
  • 8
    • 0003102944 scopus 로고
    • Maximum likelihood estimation of observer error-rates using the EM algorithm
    • Series C
    • A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the EM algorithm. Journal of the Royal Statistical Society, Series C, pages 20-28, 1979.
    • (1979) Journal of the Royal Statistical Society , pp. 20-28
    • Dawid, A.P.1    Skene, A.M.2
  • 12
  • 14
    • 84901191481 scopus 로고    scopus 로고
    • Efficient crowdsourcing for multi-class labeling
    • D. R. Karger, S. Oh, and D. Shah. Efficient crowdsourcing for multi-class labeling. In ACM SIGMETRICS, 2013.
    • (2013) ACM SIGMETRICS
    • Karger, D.R.1    Oh, S.2    Shah, D.3
  • 15
    • 84896842331 scopus 로고    scopus 로고
    • Budget-optimal task allocation for reliable crowdsourcing systems
    • D. R. Karger, S. Oh, and D. Shah. Budget-optimal task allocation for reliable crowdsourcing systems. Operations Research, 62(1): 1-24, 2014.
    • (2014) Operations Research , vol.62 , Issue.1 , pp. 1-24
    • Karger, D.R.1    Oh, S.2    Shah, D.3
  • 16
  • 19
    • 84877752474 scopus 로고    scopus 로고
    • Variational inference for crowdsourcing
    • Q. Liu, J. Peng, and A. T. Ihler. Variational inference for crowdsourcing. In NIPS, 2012.
    • (2012) NIPS
    • Liu, Q.1    Peng, J.2    Ihler, A.T.3
  • 21
    • 80053360508 scopus 로고    scopus 로고
    • Cheap and fast - But is it good? Evaluating non-expert annotations for natural language tasks
    • R. Snow, B. O'Connor, D. Jurafsky, and A. Y. Ng. Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. In Proceedings of EMNLP, 2008.
    • (2008) Proceedings of EMNLP
    • Snow, R.1    O'Connor, B.2    Jurafsky, D.3    Ng, A.Y.4
  • 23
    • 77951951247 scopus 로고    scopus 로고
    • Whose vote should count more: Optimal integration of labels from labelers of unknown expertise
    • J. Whitehill, P. Ruvolo, T. Wu, J. Bergsma, and J. R. Movellan. Whose vote should count more: Optimal integration of labels from labelers of unknown expertise. In NIPS, 2009.
    • (2009) NIPS
    • Whitehill, J.1    Ruvolo, P.2    Wu, T.3    Bergsma, J.4    Movellan, J.R.5
  • 25
    • 84919947760 scopus 로고    scopus 로고
    • Aggregating ordinal labels from crowds by minimax conditional entropy
    • D. Zhou, Q. Liu, J. C. Platt, and C. Meek. Aggregating ordinal labels from crowds by minimax conditional entropy. In Proceedings of ICML, 2014.
    • (2014) Proceedings of ICML
    • Zhou, D.1    Liu, Q.2    Platt, J.C.3    Meek, C.4
  • 26
    • 84877729010 scopus 로고    scopus 로고
    • Learning from the wisdom of crowds by minimax entropy
    • D. Zhou, J. C. Platt, S. Basu, and Y. Mao. Learning from the wisdom of crowds by minimax entropy. In NIPS, 2012.
    • (2012) NIPS
    • Zhou, D.1    Platt, J.C.2    Basu, S.3    Mao, Y.4
  • 27
    • 84899009717 scopus 로고    scopus 로고
    • Contrastive learning using spectral methods
    • J. Zou, D. Hsu, D. Parkes, and R. Adams. Contrastive learning using spectral methods. In NIPS, 2013.
    • (2013) NIPS
    • Zou, J.1    Hsu, D.2    Parkes, D.3    Adams, R.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.